A Real-Time Monitoring Method for Early Overheating of Low-Voltage Cables Based on Gas Sensor Array and Embedded System
This article proposes a cable overheating state recognition method based on a metal–oxide–semiconductor (MOS) gas sensor array. Given the limited number of original experiments and the temporal misalignment caused by heating delay, gas diffusion, and sensor–response hysteresis, this work designs a response-aligned sliding-window strategy. Specifically, a reference sensor channel is used to locate the response onset, and an offset window is then constructed to capture the rising response stage with stronger state discrimination. Multidimensional features were extracted, and both traditional machine learning models and time-series classification models were systematically evaluated. MiniRocket achieved the best offline accuracy and F1-score, while compact traditional models such as K-nearest neighbors (KNNs) and support vector machines (SVMs) also achieved accuracies above 98%. Considering offline recognition performance, online stability, and STM32 resource constraints, selected models were deployed on an STM32 platform for online recognition every 15 s. Pressure experiments further showed that the deployed model can correctly identify heating states within the evaluated pressure range of 91.2–101.3 kPa. The proposed method provides an early warning approach for cable overheating and shows potential for embedded engineering applications.